Origins of agriculture density analysis

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Figure 1 - This figures shows…

Code

1. R environment setup
2. Setting time breaks
3. Defining origins
4. Import raster data
5. GAM smoothing models
6. Compare rates between origins and not-origins
7. Compare rates between different time periods
8. Setup final figure
9. Map for final figure
10. Trend through time panel for final figure
11. Assemble and print final figure

R environment setup

Attach libraries

library(png)
library(maptools)

Checking rgeos availability: TRUE

library(raster)
library(gam)

Loading required package: splines
Loading required package: foreach
foreach: simple, scalable parallel programming from Revolution Analytics
Use Revolution R for scalability, fault tolerance and more.
http://www.revolutionanalytics.com
Loaded gam 1.14

Set working directory

setwd("~/Desktop/Botero postdoc 2016/Human density and the origins of agriculture/")

Setting time breaks

Define the times of agricultural origins

par(mar=c(0,0,0,20))
d <- readPNG("Larson_dates.png")
plot(seq(0,18, length.out = 19), seq(0,36, length.out = 19), type="n",ylim=c(0,36),xlim=c(0, 18), xaxt="n")
rasterImage(d, 0,0,18,36, interpolate=TRUE, col=d)

Start_of_early_window <- 16-12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 17-4.2
polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 34, 34, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))
polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 34, 34, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))

These dates are provided in the supplimentary information for the Larson (2014) paper. I’ve copied those values into a .csv table provided here.

domestication_times <- read.csv("Domestication timing larson 2014.csv")
dim(domestication_times)

[1] 77  8

Region	Species	Start.Exploitation	Finish.Exploitation	Start.predomestication	Finish.predomestication	Start.Domestication	Finish.Domestication
Southwest asia	Wheat	12.00	11.25	11.25	11.00	11.00	9.00
Southwest asia	Barley	12.00	11.25	11.25	10.50	10.50	9.00
Southwest asia	Lentil	12.00	11.00	11.00	10.50	10.50	9.00
Southwest asia	Pea	11.50	11.00	11.00	10.00	10.00	8.50
Southwest asia	Chickpea	11.00	10.50	10.50	10.25	10.25	8.25
Southwest asia	Broadbean	NA	NA	NA	NA	10.50	NA
Southwest asia	Flax	12.00	9.50	NA	NA	9.50	NA
Southwest asia	Olive	10.00	6.00	NA	NA	6.00	NA
Southwest asia	Sheep	12.00	10.50	10.50	9.75	9.75	8.00
Southwest asia	Goat	12.00	10.50	10.50	9.75	9.75	8.00
Southwest asia	Pig	12.00	11.50	11.50	9.75	10.25	9.00
Southwest asia	Cattle, taurine	11.50	10.50	10.50	10.25	10.25	8.00
Southwest asia	Cat	NA	NA	10.50	4.00	4.00	NA
South Asia	Tree cotton	8.50	4.50	NA	NA	4.50	NA
South Asia	Rice	8.00	5.00	5.00	4.00	4.00	2.50
South Asia	Little millet	NA	NA	NA	NA	4.50	NA
South Asia	Browntop millet	NA	NA	NA	NA	4.00	NA
South Asia	Mungbean	NA	NA	4.50	3.50	3.50	3.00
South Asia	Pigeonpea	NA	NA	NA	NA	3.50	NA
South Asia	Zebu cattle	9.00	8.00	NA	NA	8.00	6.50
South Asia	Water buffalo	6.00	4.50	NA	NA	4.50	NA
East Asia	Broomcorn Millet	10.00	8.00	NA	NA	8.00	NA
East Asia	Foxtail millet	11.50	7.50	NA	NA	7.50	NA
East Asia	Rice	10.00	8.00	8.00	7.50	7.50	5.00
East Asia	Soybean	8.50	5.50	NA	NA	5.50	4.00
East Asia	Ramie	NA	NA	NA	NA	5.25	NA
East Asia	Melon	7.00	4.00	NA	NA	4.00	3.75
East Asia	Pig	12.00	8.50	NA	NA	8.50	6.00
East Asia	Silkworm	7.00	5.25	NA	NA	5.25	NA
East Asia	Yak	NA	NA	NA	NA	4.25	NA
East Asia	Horse	7.50	6.75	6.75	5.50	5.50	4.00
East Asia	Bactrian Camel	NA	NA	NA	NA	4.50	NA
East Asia	Duck	2.50	1.00	NA	NA	1.00	NA
East Asia	Chicken	6.00	4.00	NA	NA	4.00	NA
New Guinea	Banana	10.00	7.00	7.00	4.00	4.00	NA
New Guinea	Taro	10.00	7.00	7.00	4.00	NA	NA
New Guinea	Yam	10.00	7.00	7.00	4.00	NA	NA
Africa and Arabia	Date palm	7.00	6.00	NA	NA	5.00	NA
Africa and Arabia	Sorghum	8.00	4.00	NA	NA	4.00	NA
Africa and Arabia	Pearl millet	NA	NA	NA	NA	4.50	3.50
Africa and Arabia	Fonio	NA	NA	NA	NA	2.50	NA
Africa and Arabia	Cowpea	NA	NA	NA	NA	3.75	NA
Africa and Arabia	Hyacinth bean	NA	NA	NA	NA	3.75	NA
Africa and Arabia	Rice	3.50	2.00	NA	NA	2.00	NA
Africa and Arabia	Oil palm	9.25	3.50	NA	NA	3.50	NA
Africa and Arabia	Cattle	NA	NA	9.00	7.75	7.75	6.50
Africa and Arabia	Donkey	9.00	5.50	NA	NA	5.50	3.50
Africa and Arabia	Dromedary camel	6.50	3.00	NA	NA	3.00	NA
Africa and Arabia	Guinea fowl	NA	NA	2.50	1.50	1.50	NA
North America	Squash	6.50	5.00	NA	NA	5.00	NA
North America	Sunflower	6.00	4.75	NA	NA	4.00	NA
North America	Sumpweed	6.00	4.50	NA	NA	4.00	NA
North America	Pitseed goosefoot	4.75	3.75	NA	NA	3.75	NA
Meso-america	Squash (pepo)	NA	NA	NA	NA	10.00	9.50
Meso-america	Maize	10.00	9.00	NA	NA	9.00	NA
Meso-america	Foxtail millet-grass	NA	NA	NA	NA	6.00	4.00
Meso-america	Common bean	NA	NA	NA	NA	3.00	NA
Meso-america	Avocado	NA	NA	NA	NA	3.00	NA
Meso-america	Chile pepper	NA	NA	NA	NA	3.00	NA
Meso-america	Turkey	NA	NA	NA	NA	2.00	NA
South America	Chili pepper	NA	NA	NA	NA	6.00	NA
South America	Peanut	NA	NA	NA	NA	5.00	NA
South America	Cotton	NA	NA	NA	NA	6.00	NA
South America	Coca	NA	NA	NA	NA	8.00	NA
South America	Now-minor root crops (arrowroot, leren)	NA	NA	NA	NA	9.00	NA
South America	Squash	NA	NA	NA	NA	10.00	NA
South America	Common bean	NA	NA	NA	NA	5.00	NA
South America	Lima bean	NA	NA	8.25	NA	6.00	NA
South America	Monioc	NA	NA	NA	NA	7.00	NA
South America	Sweet potato	NA	NA	NA	NA	5.00	NA
South America	White potato	7.00	4.50	NA	NA	4.00	NA
South America	Quinoa	5.00	NA	NA	NA	3.50	NA
South America	Yam	NA	NA	NA	NA	5.50	NA
South America	Llama	10.00	6.00	NA	NA	6.00	NA
South America	Alpaca	10.00	5.00	NA	NA	5.00	NA
South America	Guinea pig	NA	NA	NA	NA	5.00	NA
South America	Muscovy Duck	NA	NA	NA	NA	4.00	NA

par(mar=c(5,4,6,1))
dates <- unlist(domestication_times[3:8])
hist(dates, breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main="All dates in dataset"  )
mtext("This tells us about how evenly our evidence is distributed in time", 3, line=1)

hist(dates, breaks = 22, xlim=c(15,0), xlab="Thousand years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main="All dates in dataset with Larson(2014) date windows")
Start_of_early_window <- 12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 4.2
polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))

polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))
hist(dates, breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.2), border=adjustcolor("cornflowerblue", alpha= 0.9), main="", add=TRUE)

mtext("Early Holocene", 3, line = -1, adj=.3)
mtext("Middle Holocene", 3, line= -1, adj=.6)

par(mfrow=c(2,3), mar=c(4,4,2,0))
specific_dates <- domestication_times[3:9]
for(i in c(1, 3, 5, 2, 4, 6)){
hist(specific_dates[,i], breaks = 22, xlim=c(15,0), xlab="Thousand years ago", col=adjustcolor("cornflowerblue", alpha= 0.5), border=adjustcolor("cornflowerblue", alpha= 0.9), main= names(specific_dates)[i])
Start_of_early_window <- 12
End_of_early_window_start_of_late_window <- 8.2
End_of_late_window <- 4.2
polygon(x=c(Start_of_early_window, Start_of_early_window, End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("limegreen", alpha= 0.2), border=adjustcolor("limegreen", alpha= 0.9))
polygon(x=c( End_of_early_window_start_of_late_window, End_of_early_window_start_of_late_window, End_of_late_window, End_of_late_window), y=c(0, 30, 30, 0), col=adjustcolor("firebrick", alpha= 0.2), border=adjustcolor("firebrick", alpha= 0.9))
hist(specific_dates[,i], breaks = 22, xlim=c(15,0), xlab="K years ago", col=adjustcolor("cornflowerblue", alpha= 0.2), border=adjustcolor("cornflowerblue", alpha= 0.9), main="", add=TRUE)
}

I’m creating new rows for this table, combining dates in different ways to make the CDFs below look more authentic. This makes it so that pre-ag always happens before post-ag. What I’ve done is given the later date to the earlier date when those dates are missing.

h <- which(is.na(domestication_times[,3]))
domestication_times <- cbind(domestication_times, rep(NA, length(domestication_times[,1])))
domestication_times[,9] <- domestication_times[,3]
domestication_times[h,9] <- domestication_times[h,7]
colnames(domestication_times)[9] <- "adopt exploitation date"
domestication_times[,10] <- domestication_times[,7]
domestication_times[which(is.na(domestication_times[,10])),10] <- 0
colnames(domestication_times)[10] <- "start of ag"
#save(domestication_times, file="~/Desktop/Human density and the origins of agriculture/Domestication timing larson 2014.Rdata")

I think these are best described by a cummulative distribution, showing how they accumulate over time.

for(i in 1:8){
type_number <- i
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    print(levels(domestication_times$Region)[ type_number])
    print(match)
    print(j)
}

[1] "Africa and Arabia"
 [1] 7.00 8.00 4.50 2.50 3.75 3.75 3.50 9.25 7.75 9.00 6.50 1.50
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   0.25,   1.25,  ...,   6.75,   7.75
[1] "East Asia"
 [1] 10.00 11.50 10.00  8.50  5.25  7.00 12.00  7.00  4.25  7.50  4.50  2.50  6.00
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,    0.5,      2,  ...,   7.75,    9.5
[1] "Meso-america"
[1] 10 10  6  3  3  3  2
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,      4,      7,      8
[1] "New Guinea"
[1] 10 10 10
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:1] =      0
[1] "North America"
[1] 6.50 6.00 6.00 4.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:3] =      0,    0.5,   1.75
[1] "South America"
 [1]  6.0  5.0  6.0  8.0  9.0 10.0  5.0  6.0  7.0  5.0  7.0  5.0  5.5 10.0 10.0  5.0  4.0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
[1] "South Asia"
[1] 8.5 8.0 4.5 4.0 3.5 3.5 9.0 6.0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:7] =      0,    0.5,      1,  ...,      5,    5.5
[1] "Southwest asia"
 [1] 12.0 12.0 12.0 11.5 11.0 10.5 12.0 10.0 12.0 12.0 12.0 11.5  4.0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:6] =      0,    0.5,      1,  ...,      2,      8

par(mfcol=c(2,5), mar=c(4,0,5,0))
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

for(i in 1:8){
type_number <- i
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    #print(j)
    
plot(0,0, xlim=c(15,0), ylim=c(0,100), ylab="Percent of species that will eventually \n be domesticated in a region", xlab="Thousand years ago", main=levels(domestication_times$Region)[ type_number], type="n", yaxt="n")
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 100 * (c(0, j(seq(0, maxer, length.out=100))))
lines(x_seq, y_seq,  ylim=c(-1,1))
polygon(c(0, x_seq), c(0, y_seq), border=adjustcolor("cornflowerblue",alpha=1), col=adjustcolor("cornflowerblue", alpha=0.2))
if(i == 2 | i == 1)axis(2)
if(i == 3)mtext("Cummulative distribution function for the accumulation of domesticates", 3, line=3.8, col="cornflowerblue")
}

par(mfcol=c(2,5), mar=c(4,0,5,0))
plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

plot(0,0, type="n", xaxt="n", xlab="", bty="n")
mtext("Percent of species that will eventually \n be domesticated in a region", 2, line=-5, cex=0.5)

for(i in 1:8){
type_number <- i
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    #print(j)
    
plot(0,0, xlim=c(15,0), ylim=c(0,100), ylab="Percent of species that will eventually \n be domesticated in a region", xlab="Thousand years ago", main=levels(domestication_times$Region)[ type_number], type="n", yaxt="n")
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 100 * (c(0, j(seq(0, maxer, length.out=100))))
lines(x_seq, y_seq,  ylim=c(-1,1))
polygon(c(0, x_seq), c(0, y_seq), border=adjustcolor("cornflowerblue",alpha=1), col=adjustcolor("cornflowerblue", alpha=0.2))
abline(v= maxer - quantile(j)[2], col="limegreen", lwd=2)
if(i == 2 | i == 1)axis(2)
if(i == 2)mtext("25%", 3, line=3.5, adj=-1, col="limegreen")
if(i == 3)mtext("Cummulative distribution function for the accumulation of domesticates", 3, line=3.8, col="cornflowerblue")
if(i == 4)mtext("Choose a y to predict an x", 3, line=3.3, col="cornflowerblue")
    break_one <- maxer
            break_two <- maxer - quantile(j)[2]
                
    polygon(x=c(break_two, break_two, break_one, break_one), y=c(0, 1, 1, 0), col=adjustcolor("cornflowerblue", alpha=0.2), border=adjustcolor("cornflowerblue",alpha=1))
            lines(x=c(break_two, break_two), y=c(0,-1), col="cornflowerblue")
            abline(h = 25, col="limegreen", lwd=2)
}

Make this a function. There is a choice of two methods here. At the end of this section we need to print the desision we’re passing to the later analyses.

Defining origins

origins

class       : SpatialPolygonsDataFrame 
features    : 20 
extent      : -104.7263, 144.9554, -26.31802, 43.00233  (xmin, xmax, ymin, ymax)
coord. ref. : NA 
variables   : 8

no non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NAno non-missing arguments, returning NA

names       :     CONTINENT, SQMI, SQKM, OID, CONTINEN_2,  SQMI_2,  SQKM_2, OID_2 
min values  :     C/S_Andes,   NA,   NA,  NA,     Europe, 3821854, 9898597,     7 
max values  : West Africa T,   NA,   NA,  NA,     Europe, 3821854, 9898597,     7 

library(maps)
map()
map(origins, add=TRUE, fill=TRUE, col=adjustcolor("cornflowerblue", alpha=1))

database does not (uniquely) contain the field 'name'.

map()
d <- readPNG("Larson_origins.png")
rasterImage(d, -180, -90, 180, 110, interpolate=TRUE, col=d)

map(add=TRUE)
map(origins, add=TRUE, fill=TRUE, col=adjustcolor("cornflowerblue", alpha=1))

database does not (uniquely) contain the field 'name'.

This is obviously a bad projection fit right now.

Import raster data

#subset and reorder origins. This is currently done at the end of the plot but should be moved forward.
# Load data for population density
load("PopD_all_December.rdata")
PopD.ALL

class       : RasterStack 
dimensions  : 288, 720, 207360, 18  (nrow, ncol, ncell, nlayers)
resolution  : 0.5, 0.5  (x, y)
extent      : -180, 180, -60, 84  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
names       :        fourK,        fiveK,         sixK,       sevenK,       eightK,        nineK,         tenK,      elevenK,      twelveK,    thirteenK,    fourteenK,     fifteenK,     sixteenK,   seventeenK,    eighteenK, ... 
min values  : 5.611358e-07, 1.067142e-06, 2.508241e-06, 6.317553e-06, 2.286934e-05, 7.631922e-05, 1.272693e-04, 2.118215e-04, 2.602175e-04, 3.226203e-04, 4.390267e-04, 5.572032e-04, 7.313966e-04, 8.286005e-04, 8.297062e-04, ... 
max values  :     2.051069,     2.013452,     2.142908,     1.888403,     1.863014,     1.880628,     1.650615,     1.678033,     1.697732,     1.499115,     1.517264,     1.443677,     1.464867,     1.453581,     1.436394, ... 

# Extract data to a matrix
Pop <- values(PopD.ALL)
r <- raster(PopD.ALL, 1)
r

class       : RasterLayer 
dimensions  : 288, 720, 207360  (nrow, ncol, ncell)
resolution  : 0.5, 0.5  (x, y)
extent      : -180, 180, -60, 84  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : fourK 
values      : 5.611358e-07, 2.051069  (min, max)

GAM smoothing models

Justification for General Adative Models.

We need to justify our decision to use a GAM over other models. This should include citations to back up those arguments.

Fit and plot GAM model with different degrees of freedom

We should make our decisions very transparent here. We should be able to justify our decision of 3 degrees of freedom over other possible values.

Density projections

# Get the predctions from Population_trend script
load("prediction.RData")
# Read the polygons
origins <- readShapePoly('Origins_updated.shp')
# Extract data
cells <- do.call(rbind, sapply(per.origin, subset, select = 1))
#cells
g.means <- apply(prediction[-cells, ], 2, mean, na.rm = TRUE) 
g.gams <- apply(prediction[-cells, ], 2, sd, na.rm = TRUE)
g.means2 <- apply(prediction[cells, ], 2, mean, na.rm = TRUE) 
g.gams2 <- apply(prediction[cells, ], 2, sd, na.rm = TRUE)
#pdf("Global_pop_trend_comparisson.pdf", width = 25, height = 20)
par(mar = c(5, 7, 7, 5))
plot(seq(0, 1, length.out = length(time)) ~ time, col = "white", main = "GLOBAL",
     xlim = c(21, 4), ylab = "Population Density (standardized)", 
     xlab = "Thousand of years ago", cex.lab = 1, cex.main = 1, cex.axis = 1)
down <- g.means - g.gams
up <- g.means + g.gams
lines(y = down, x = time, lty = 3, col = "gray40", lwd = 3)

lines(y = up, x = time, lty = 3, col = "gray40", lwd = 3)
lines(y = g.means, x = time, lwd = 4)

lines(y = g.means2, x = time, lwd = 3, col = "red")
down2 <- g.means2 - g.gams2
up2 <- g.means2 + g.gams2
lines(y = down2, x = time, lty = 3, col = "red", lwd = 3)

lines(y = up2, x = time, lty = 3, col = "red", lwd = 3)
polygon(cbind(c(12, 8.2, 8.2, 12, 12), c(-1, -1, 2, 2, -1)),
        col = rgb(0, 1, 0, alpha = .2), border = F)

polygon(cbind(c(8.2, 4.2, 4.2, 8.2, 8.2), c(-1, -1, 2, 2, -1)),
        col = rgb(.28, 0, .28, alpha = .2), border = F)

#dev.off()

# need to add a global mean, an everything but the origins mean, and a buffer around the origins mean. 

# Read the polygons
origins <- readShapePoly('Origins_updated.shp')

# Extract data
per.origin <- extract(r, origins, cellnumber = TRUE, buffer = 100000)
names(per.origin) <- origins@data[, 1]

# Function standardization
std <- function(x) {
  b <- (x - min(x)) / (max(x) - min(x))
  return(rev(b))
}

# Calculating mean and 
global.means <- global.SD <- list()

for (j in 1:length(per.origin)) {
  #print(j)
  originI <- Pop[per.origin[[j]][, 1], ]
  time <- 21:4
  originI <- na.exclude(originI)
  b <- apply(originI, 1, std)
  nJ <- nrow(originI)
  predictions <- matrix(nrow = nJ, ncol = length(time))
  colnames(predictions) <- as.character(time)
  for(i in 1:nJ) {
    
    # Need to show a gradient of these df values. 
    model <- gam(b[, i] ~ s(time, df = 15))
    col <- sample(rainbow(100), 1)
    predictions[i, ] <- predict(model)
  }
  global.means[[j]] <- apply(predictions, 2, mean) 
  global.SD[[j]] <- apply(predictions, 2, sd)
}



origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle

names(global.means) <- paste(names(per.origin), "Means")
names(global.SD) <- paste(names(per.origin), "SD")

plot(global.means[[1]], col=adjustcolor("cornflowerblue", alpha=0.8), pch=names(global.means[[1]]), type="b", xlab="year", ylab="Density", xaxt="n")
axis(1, at=seq(1,18, by=1), label=rev(seq(4, 21, by=1)))

global.means

$`W_African_Sav Means`
         21          20          19          18          17          16          15 
0.277236126 0.268146097 0.229965375 0.325239440 0.099371319 0.003780048 0.028853343 
         14          13          12          11          10           9           8 
0.096533656 0.170551592 0.236171347 0.329881362 0.408474455 0.933326291 1.020506850 
          7           6           5           4 
0.850021182 0.257169819 0.326479020 0.507805633 

$`Sudanic_Savan Means`
        21         20         19         18         17         16         15         14 
0.01698931 0.01658303 0.01377887 0.01873437 0.02676287 0.05040199 0.11185374 0.17117563 
        13         12         11         10          9          8          7          6 
0.22976620 0.29037257 0.39363331 0.46512887 0.95241742 1.01093274 0.89309987 0.35578816 
         5          4 
0.54615181 0.75726423 

$`West Africa T Means`
        21         20         19         18         17         16         15         14 
0.01898613 0.02119569 0.01844946 0.01229571 0.04011481 0.06425710 0.12281442 0.18024456 
        13         12         11         10          9          8          7          6 
0.23876336 0.29694678 0.39395913 0.47594903 0.87575791 0.90858669 0.87451414 0.58149682 
         5          4 
0.74507230 0.95664612 

$`Ethipian plat Means`
        21         20         19         18         17         16         15         14 
0.09045898 0.09039249 0.08631005 0.08939636 0.10102942 0.11280965 0.16544499 0.23758729 
        13         12         11         10          9          8          7          6 
0.33576370 0.42248406 0.56394916 0.62406635 0.94001810 0.95457968 0.92035401 0.56309708 
         5          4 
0.60472053 0.91781126 

$`NA Means`
        21         20         19         18         17         16         15         14 
0.66311422 0.66055581 0.64267837 0.68037338 0.57919737 0.44972219 0.29206295 0.19322360 
        13         12         11         10          9          8          7          6 
0.13282795 0.11186128 0.07109712 0.13037398 0.67120744 0.78675575 0.73905383 0.83210726 
         5          4 
0.65518007 0.51579662 

$`E_North_Ameri Means`
         21          20          19          18          17          16          15 
0.008935466 0.010491576 0.013253014 0.005429808 0.033277010 0.067607455 0.151565641 
         14          13          12          11          10           9           8 
0.252887930 0.364503617 0.473544250 0.693964954 0.767791047 0.692619560 0.694781027 
          7           6           5           4 
0.698626071 0.419432398 0.858741718 1.009851858 

$`New_Guinea Means`
        21         20         19         18         17         16         15         14 
0.28090841 0.27887032 0.26769422 0.28147012 0.25274529 0.21472830 0.16708455 0.11597030 
        13         12         11         10          9          8          7          6 
0.08979978 0.08089207 0.01244318 0.04012938 0.56658193 0.60497997 0.64152040 0.95583067 
         5          4 
0.70849108 0.66240742 

$`Mesoamerica Means`
         21          20          19          18          17          16          15 
0.016195001 0.020624506 0.021438999 0.009346113 0.054411490 0.103081131 0.194265351 
         14          13          12          11          10           9           8 
0.278924138 0.363943244 0.439908974 0.568842547 0.615137399 0.832150879 0.835544620 
          7           6           5           4 
0.803694000 0.462771112 0.868405845 1.009287780 

$`N_Lowland_SA Means`
        21         20         19         18         17         16         15         14 
0.19768919 0.19404181 0.19098494 0.19612871 0.19641766 0.18706802 0.16050661 0.13414511 
        13         12         11         10          9          8          7          6 
0.10788427 0.09115094 0.02059283 0.04595208 0.59600575 0.62988681 0.65634027 0.95041179 
         5          4 
0.71231938 0.76245058 

$`NW_Lowland_SA Means`
        21         20         19         18         17         16         15         14 
0.30216048 0.30473388 0.30145828 0.30660886 0.29457158 0.26911208 0.24906510 0.20628929 
        13         12         11         10          9          8          7          6 
0.15615286 0.12041864 0.03913096 0.03081728 0.29794031 0.39915852 0.50023797 0.94635445 
         5          4 
0.69180450 0.60391276 

$`Sava_W_India Means`
         21          20          19          18          17          16          15 
0.010624383 0.011415291 0.016124491 0.003918883 0.041128894 0.079911864 0.143474824 
         14          13          12          11          10           9           8 
0.194472002 0.258964535 0.328023269 0.385019948 0.438200163 0.701906926 0.887863530 
          7           6           5           4 
0.840257998 0.463636951 0.824692040 1.006026208 

$`S_India Means`
         21          20          19          18          17          16          15 
0.011562958 0.018775299 0.021723311 0.007064695 0.040707283 0.071306979 0.139798009 
         14          13          12          11          10           9           8 
0.225664613 0.281610839 0.348147871 0.403540117 0.477872218 0.800247791 0.852607259 
          7           6           5           4 
0.838151683 0.653173598 0.915365428 1.005984490 

$`Ganges_E_Indi Means`
         21          20          19          18          17          16          15 
0.011336515 0.012643677 0.013706701 0.007068004 0.034046963 0.061879159 0.107610274 
         14          13          12          11          10           9           8 
0.154284669 0.214022369 0.261411689 0.354911485 0.405502151 0.701022088 0.856628658 
          7           6           5           4 
0.857644757 0.634690322 0.846206514 1.003631222 

$`Chinese_loess Means`
         21          20          19          18          17          16          15 
0.009851651 0.010480832 0.009006839 0.011611970 0.011877916 0.044124301 0.071626358 
         14          13          12          11          10           9           8 
0.086101379 0.149418292 0.211519249 0.266988439 0.311227904 0.707572309 0.849429071 
          7           6           5           4 
0.906202082 0.923045545 0.991676077 0.973623793 

$`Japanese Means`
        21         20         19         18         17         16         15         14 
0.43296608 0.43672128 0.43035780 0.44830208 0.40355896 0.34383951 0.27578618 0.20037155 
        13         12         11         10          9          8          7          6 
0.12621768 0.08676529 0.01311096 0.07301075 0.80925018 0.90329694 0.86470455 0.98192628 
         5          4 
0.86398717 0.75827885 

$`Lower-MiddleY Means`
        21         20         19         18         17         16         15         14 
0.01208663 0.01575036 0.01411414 0.01178427 0.03201081 0.05071188 0.09189183 0.12487305 
        13         12         11         10          9          8          7          6 
0.15860810 0.20452035 0.24395821 0.28747052 0.68074944 0.77375375 0.84134458 0.69834173 
         5          4 
0.90780965 1.00418953 

$`South trop ch Means`
        21         20         19         18         17         16         15         14 
0.01671345 0.01747534 0.01513935 0.01802837 0.01568232 0.01227508 0.03863965 0.09161156 
        13         12         11         10          9          8          7          6 
0.11464529 0.22358808 0.29758793 0.37545866 0.80524910 0.93801022 0.94552239 0.91083022 
         5          4 
0.96600401 0.98801607 

$`NA Means`
        21         20         19         18         17         16         15         14 
0.05066894 0.05230038 0.05292235 0.05835717 0.04889094 0.03936478 0.03905613 0.13386001 
        13         12         11         10          9          8          7          6 
0.18460943 0.19718384 0.21980313 0.35557000 0.76574625 0.92168159 0.90805891 0.88692400 
         5          4 
0.94897297 0.95173354 

$`Southwes amaz Means`
        21         20         19         18         17         16         15         14 
0.19781479 0.19662496 0.18993796 0.19064183 0.20111408 0.19240561 0.16943207 0.14150350 
        13         12         11         10          9          8          7          6 
0.11674148 0.10887194 0.06376160 0.07888967 0.60360527 0.61746807 0.65100738 0.92881641 
         5          4 
0.92751249 0.92126285 

$`C/S_Andes Means`
        21         20         19         18         17         16         15         14 
0.04013846 0.03976948 0.04037451 0.04009385 0.05985825 0.07147904 0.10113090 0.11532751 
        13         12         11         10          9          8          7          6 
0.12894659 0.14465050 0.14431787 0.18643630 0.67781044 0.70463762 0.73107986 0.89570492 
         5          4 
0.94294701 0.96109400 

global.SD

$`W_African_Sav SD`
        21         20         19         18         17         16         15         14 
0.17269901 0.16622415 0.13867831 0.18544129 0.05180233 0.01767435 0.02327218 0.03336443 
        13         12         11         10          9          8          7          6 
0.03541682 0.03519989 0.04853473 0.06084298 0.02424955 0.01252286 0.03085380 0.08163591 
         5          4 
0.13119633 0.13670807 

$`Sudanic_Savan SD`
        21         20         19         18         17         16         15         14 
0.01596981 0.01573248 0.01309909 0.01660033 0.01596607 0.02560278 0.04523896 0.05682794 
        13         12         11         10          9          8          7          6 
0.06526664 0.06715609 0.05264207 0.05237042 0.01249360 0.02168305 0.03570080 0.14230242 
         5          4 
0.12537535 0.13709822 

$`West Africa T SD`
        21         20         19         18         17         16         15         14 
0.01673219 0.01578994 0.01481378 0.01136136 0.01969958 0.02756898 0.04194365 0.05049252 
        13         12         11         10          9          8          7          6 
0.05967940 0.06586580 0.08288599 0.06990498 0.08183580 0.09246568 0.05351030 0.12807501 
         5          4 
0.07954410 0.06756217 

$`Ethipian plat SD`
        21         20         19         18         17         16         15         14 
0.16291219 0.16041160 0.15174222 0.17052526 0.12030900 0.08321087 0.10179650 0.15003746 
        13         12         11         10          9          8          7          6 
0.20258904 0.22923829 0.26964589 0.25334530 0.04474472 0.05250856 0.04248866 0.18245586 
         5          4 
0.07985206 0.10230497 

$`NA SD`
        21         20         19         18         17         16         15         14 
0.31724393 0.31534358 0.30865208 0.32400309 0.29351537 0.25850263 0.20335450 0.15240433 
        13         12         11         10          9          8          7          6 
0.09519069 0.06447755 0.08636095 0.12921181 0.14130818 0.18480720 0.14252473 0.14154360 
         5          4 
0.09762597 0.11603513 

$`E_North_Ameri SD`
         21          20          19          18          17          16          15 
0.008145198 0.008515398 0.009294010 0.004473189 0.011508708 0.015625265 0.022584915 
         14          13          12          11          10           9           8 
0.025198449 0.024445301 0.022727993 0.032069814 0.040761082 0.052481734 0.052946139 
          7           6           5           4 
0.039564858 0.037054913 0.026284034 0.001341543 

$`New_Guinea SD`
         21          20          19          18          17          16          15 
0.082195833 0.082863879 0.078154136 0.082808803 0.078094798 0.071787935 0.053393900 
         14          13          12          11          10           9           8 
0.049463057 0.038550008 0.027472188 0.011530095 0.008002951 0.087275661 0.089553441 
          7           6           5           4 
0.056219416 0.005523272 0.033304801 0.044605179 

$`Mesoamerica SD`
         21          20          19          18          17          16          15 
0.021153785 0.022939364 0.016641861 0.011819479 0.022523991 0.035286885 0.061362074 
         14          13          12          11          10           9           8 
0.085539532 0.103613886 0.114220951 0.129846632 0.128105209 0.045201018 0.055611867 
          7           6           5           4 
0.048259340 0.113311245 0.039613106 0.003286985 

$`N_Lowland_SA SD`
        21         20         19         18         17         16         15         14 
0.10330655 0.10034704 0.10171417 0.10623824 0.09774895 0.08977896 0.06518589 0.04894999 
        13         12         11         10          9          8          7          6 
0.03813627 0.02881662 0.02613161 0.02178818 0.11032995 0.12044717 0.10686664 0.01704901 
         5          4 
0.08244668 0.12104669 

$`NW_Lowland_SA SD`
         21          20          19          18          17          16          15 
0.130739697 0.132907733 0.132009694 0.133691638 0.127524837 0.113005201 0.069851646 
         14          13          12          11          10           9           8 
0.057568162 0.059602563 0.063103075 0.072715681 0.066751796 0.078178507 0.093155849 
          7           6           5           4 
0.058635826 0.005804702 0.045253754 0.052150366 

$`Sava_W_India SD`
         21          20          19          18          17          16          15 
0.005786955 0.005220734 0.006138490 0.001834000 0.008370193 0.014809019 0.023478907 
         14          13          12          11          10           9           8 
0.029202441 0.033611577 0.049744933 0.058186494 0.075529590 0.082953275 0.049622009 
          7           6           5           4 
0.042997287 0.051914027 0.044540102 0.007729331 

$`S_India SD`
         21          20          19          18          17          16          15 
0.011762488 0.015787105 0.011814528 0.006555100 0.012978839 0.012634400 0.017908110 
         14          13          12          11          10           9           8 
0.048727505 0.042995471 0.028245014 0.035767644 0.056730798 0.038340653 0.034123821 
          7           6           5           4 
0.031387785 0.026180677 0.027622224 0.001124058 

$`Ganges_E_Indi SD`
         21          20          19          18          17          16          15 
0.009996104 0.010161509 0.010487678 0.006416099 0.014754648 0.023414633 0.037629153 
         14          13          12          11          10           9           8 
0.051351876 0.064161762 0.070084828 0.099905287 0.108208542 0.147219682 0.125484765 
          7           6           5           4 
0.102598416 0.092287701 0.093801548 0.002533703 

$`Chinese_loess SD`
         21          20          19          18          17          16          15 
0.010457617 0.009801106 0.007833340 0.007565184 0.009443886 0.022651911 0.021857693 
         14          13          12          11          10           9           8 
0.014895515 0.039283031 0.031720217 0.046021275 0.044369415 0.103392912 0.099384943 
          7           6           5           4 
0.057457163 0.049823920 0.006320118 0.023000503 

$`Japanese SD`
         21          20          19          18          17          16          15 
0.173874868 0.163342130 0.149019750 0.150323445 0.145901292 0.127512094 0.070015226 
         14          13          12          11          10           9           8 
0.055198915 0.052044874 0.048228880 0.039058313 0.058440859 0.094423895 0.044348710 
          7           6           5           4 
0.039955363 0.004581652 0.034395274 0.052817882 

$`Lower-MiddleY SD`
         21          20          19          18          17          16          15 
0.012157572 0.013518960 0.012466976 0.011512224 0.014174312 0.016363557 0.021436642 
         14          13          12          11          10           9           8 
0.032419006 0.039212090 0.043682275 0.056409379 0.056218411 0.050885376 0.046217941 
          7           6           5           4 
0.029361739 0.048027193 0.040810241 0.002212724 

$`South trop ch SD`
        21         20         19         18         17         16         15         14 
0.01251537 0.01380555 0.01211271 0.01426391 0.01218339 0.01093822 0.02713782 0.04214790 
        13         12         11         10          9          8          7          6 
0.04932471 0.06819564 0.07256793 0.09153205 0.10740169 0.09418365 0.06698325 0.06470289 
         5          4 
0.02305945 0.02213715 

$`NA SD`
        21         20         19         18         17         16         15         14 
0.06537329 0.06279184 0.05844069 0.06515297 0.04831535 0.03065137 0.02933002 0.06506202 
        13         12         11         10          9          8          7          6 
0.08780131 0.10016928 0.12958624 0.13599030 0.05745369 0.04854576 0.04360634 0.09354232 
         5          4 
0.04283214 0.07353796 

$`Southwes amaz SD`
        21         20         19         18         17         16         15         14 
0.10946970 0.11244845 0.11233937 0.11449979 0.09609846 0.07918164 0.04897765 0.04514773 
        13         12         11         10          9          8          7          6 
0.05154662 0.06454150 0.08797545 0.08539320 0.04617412 0.04963616 0.04439196 0.07708328 
         5          4 
0.04741189 0.06601495 

$`C/S_Andes SD`
        21         20         19         18         17         16         15         14 
0.06693888 0.06209531 0.06430201 0.06473326 0.06066622 0.05122856 0.05624921 0.06267174 
        13         12         11         10          9          8          7          6 
0.06421460 0.06696766 0.08404686 0.08570244 0.10382184 0.11311800 0.09010230 0.05474039 
         5          4 
0.07111518 0.07379329 

Productivity

# Load patricks productivity PCA data
load('Productivity_ALL.RDATA')
# Load origin shapefiles
origins <- readShapePoly('Origins_updated.shp')
origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle
# Extract the data
prod.origin <- extract(Productivity.ALL, origins)

NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files

# Mean and SD per region
means <- lapply(prod.origin, colMeans, na.rm = TRUE)
sds <- lapply(prod.origin, sd, na.rm = TRUE)
names(means) <- origins@data$CONTINENT
ymax <- max(unlist(means))
ymin <- min(unlist(means))
time <- 4:21
# Plot
#pdf("productivity.pdf", 20, 30) 
par(mfrow = c(5, 4), mar = c(2, 2, 2, 0))
for (i in 1:length(means)) {
  plot(y = means[[i]], x = time, xlim = c(21, 4), ylim = c(ymin, ymax),
       main = names(means)[i], cex.main = 1, cex.lab = 1, cex.axis = 1,
       ylab = "Productivity (PCA axis)", xlab = "Thousand of years ago (k)",
       pch = 20, lwd = 1, type = "l", 
       col = c("purple", "green")[origin.time.region[i]])
  up <- sds[[i]] + means[[i]]
  down <-  means[[i]] - sds[[i]]
  lines(up ~ time, lty = 2)
  lines(down ~ time, lty = 2)
  
}

#dev.off()

Compare rates between origins and not-origins

Compare rates between different time periods

Setup final figure

Frame in the layout

a <- layout(matrix(c(
    1, 1, 1, 1, 1, 1, 1, 1,
    3,  6, 7, 8, 9, 10, 11, 4, 
    3,  5, 5, 5, 5, 5, 5,   4, 
    3,  12, 13, 14, 15, 16, 17, 4,
    2, 2, 2, 2, 2, 2, 2, 2
    ), 5, 8, byrow=TRUE), width=c(1, 1, 1, 1, 1, 1, 1, 1), height=c(0.5, 1, 1.5, 1, 0.5))
layout.show(a)

Make blank template plots

frameplot <- function(){
    plot(21:0,rep(0, 22), xlim=c(17,4), ylim=c(0, 2.25), type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}
frameplot_bottom <- function(){
    plot(21:0,rep(0, 22), xlim=c(17,4), ylim=c(-0.25, 2), type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}

frameplot()

frameplot_bottom()

map for final figure

Make the map for the center panel (#5 on layout panel)

d <- readPNG("earth.png")

png(file=paste("40962.png",sep=""),width=2000,height=1000, bg="transparent")
par(mar=c(0,0,0,0))
plot(seq(-180, 180, length.out = 19), seq(-90, 90, length.out = 19), type="n",xlim=c(-180, 180),ylim=c(-90, 90), xaxt="n")

rasterImage(d, -180, -90, 180, 90, interpolate=TRUE, col=d)

polygon(x=c(-180,-180, 180,180), y=c(-90, 90, 90, -90), col=adjustcolor("white", alpha=0.1))
#rasterImage(d, -13.5, -13.5, 375, 375, interpolate=TRUE, col=d)
plot(origins_subset, add=TRUE, col=adjustcolor("white", alpha=.8), xaxt="n", border="white", lwd=4) #still need to reproject!!!
dev.off()

Trend through time panel

Setup the plot template for small panel plots (#6-17 on layout panel)

###################
type_number <- 8
complex_figure <- function(type_number, i, means, sds){
                        
if(i < 6)   polygon(x=c(12,12,8.2,8.2), y=c(-1,3,3,-1), col=adjustcolor("cornflowerblue", alpha=0.4), border=NA)                    
if(i > 5)   polygon(x=c(8.2,8.2,4.2,4.2), y=c(-1,3,3,-1), col=adjustcolor("limegreen", alpha=0.4), border=NA)
                                    
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 9]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    print(j)
    
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- -c(0, j(seq(0, maxer, length.out=100)))
#lines(x_seq, y_seq, type="l", ylim=c(-1,1))
#polygon(c(0, x_seq), c(0, y_seq), border="black", col=adjustcolor("cornflowerblue", alpha=0.5))
#abline(v= maxer - quantile(j)[2])
    
    break_one_1 <- maxer
            break_two_1 <- maxer - quantile(j)[2]
                
#   polygon(x=c(break_two_1, break_two_1, break_one_1, break_one_1), y=c(0, 1, 1, 0), col=adjustcolor("cornflowerblue", alpha=0.5), border=NA)
            
    match <- domestication_times[ which(domestication_times$Region == levels(domestication_times$Region)[ type_number]), 10]
    maxer <- max(match, na.rm=TRUE)
    j <- ecdf(maxer-match)
    print(j)
    
x_seq <- rev(c(0,seq(0, maxer, length.out=100)))
y_seq <- 2+c(0, j(seq(0, maxer, length.out=100)))
#lines(x_seq, y_seq)
#polygon(c(0, x_seq), c(2, y_seq), border="black", col=adjustcolor("limegreen", alpha=0.5))
    
    break_one_2 <- maxer
            break_two_2 <- maxer - quantile(j)[2]
                
#   polygon(x=c(break_two_2, break_two_2, break_one_2, break_one_2), y=c(1, 2, 2, 1), col=adjustcolor("limegreen", alpha=0.5), border=NA)
            
    
        #abline(v=11)
    type <- 1
        
        if(type == 1){
    x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
    scaled <- scale(x , center=FALSE)
    meanss <- scaled[1:18]
    sdss_plus <- scaled[19:36]
    sdss_minus <- scaled[37:54]
    #abline(v=10, col="red")
    length(scaled)
    #lines(4:21, means[[i]] + sds[[i]])
    #polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")
    polygon(x=c(21:4,4:21), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white") 
    }
    
    if(type == 2){
    x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
    scaled <- x + 1 #scale(x , center=FALSE)
    meanss <- scaled[1:18]
    sdss_plus <- scaled[19:36]
    sdss_minus <- scaled[37:54]
    #abline(v=10, col="red")
    length(scaled)
    #lines(4:21, means[[i]] + sds[[i]])
    polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")    
    }
if(type == 3){
    x <- c(means[[i]] , means[[i]]  + abs(sds[[i]]), means[[i]]  - abs(sds[[i]]))
    scaled <- x #scale(x , center=FALSE)
    meanss <- scaled[1:18]
    sdss_plus <- scaled[19:36]
    sdss_minus <- scaled[37:54]
    #abline(v=10, col="red")
    length(scaled)
    #lines(4:21, means[[i]] + sds[[i]])
    polygon(x=c(4:21, 21:4), y=c(sdss_plus, rev(sdss_minus)), col=adjustcolor("firebrick", alpha=1), border="white")    
    }
    
    
means_long_y <- c(1,1,1,1,1, meanss)
means_long_x <- c(0:4, 4:21)
 
            break_one <- break_one_2
            break_two <- break_two_2
        #       polygon(x=c(break_one, break_one, 22, 22), y=c(1, 2, 2, 1), col=adjustcolor("white", alpha=0.8), border=NA)
        #       polygon(x=c(break_two, break_two, break_one, break_one), y=c(1, 2, 2, 1), col=adjustcolor("white", alpha=0), border=NA)
            #   polygon(x=c(-1,-1, break_two , break_two), y=c(1.9, 3.1, 3.1, 1.9), col=adjustcolor("white", alpha=0.8), border=NA) 
                #abline(v= break_one, col="white")
                #abline(v= break_two, col="white")
                
                break_one <- break_one_1
            break_two <- break_two_1
        #       polygon(x=c(break_one, break_one, 22, 22), y=c(0, 1, 1, 0), col=adjustcolor("white", alpha=0.8), border=NA)
        #       polygon(x=c(break_two, break_two, break_one, break_one), y=c(0, 1, 1, 0), col=adjustcolor("white", alpha=0), border=NA)
            #   polygon(x=c(-1,-1, break_two , break_two), y=c(-1.1, .1, .1, -1.1), col=adjustcolor("white", alpha=0.8), border=NA) 
                #abline(v= break_one, col="white")
                #abline(v= break_two, col="white")
                
#lines(x=c(break_one_2, break_one_2), y=c(1,3), col="white")
#lines(x=c(break_one_1, break_one_1), y=c(1,-1), col="white")
#lines(x=c(break_two_2, break_two_2), y=c(1,3), col="white")
#lines(x=c(break_two_1, break_two_1), y=c(1,-1), col="white") 
#lines(4:21, meanss)
    lines(21:4, meanss)
    
}

frameplot()
complex_figure(7, 1, global.means, global.SD) 

Empirical CDF 
Call: ecdf(maxer - match)
 x[1:7] =      0,    0.5,      1,  ...,      5,    5.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,    3.5,      4,    4.5

axis(1)
axis(2)

Assemble the figure

Assemble the figure

quartz(width=8, height=8)
layout(matrix(c(
    1, 1, 1, 1, 1, 1, 1, 1,
    3,  6, 7, 8, 9, 10, 11, 4, 
    3,  5, 5, 5, 5, 5, 5,   4, 
    3,  12, 13, 14, 15, 16, 17, 4,
    2, 2, 2, 2, 2, 2, 2, 2
    ), 5, 8, byrow=TRUE), width=c(1, 1, 1, 1, 1, 1, 1, 1), height=c(0.5, 1, 1.5, 1, 0.5))
par(mar=c(0,0,0,0))
# 1-4 label margins
blankplot <- function(){
    
    plot(0,0, xlim=c(4,21), ylim=c(1, 1.25), bty="n", type="n", xaxt="n", yaxt="n", xlab="", ylab="")
}
blankplot()
blankplot()

blankplot()
blankplot()

origins <- readShapePoly('Origins_updated.shp')
origin.time.region <- c(2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 
                        2, 2, 1, 2, 2, 2, 2, 2, 2, 2) # 1 = early; 2 = middle
as.character(origins$CONTINENT)

 [1] "W_African_Sav" "Sudanic_Savan" "West Africa T" "Ethipian plat" "Fertile_Cresc"
 [6] "E_North_Ameri" "New_Guinea"    "Mesoamerica"   "N_Lowland_SA"  "NW_Lowland_SA"
[11] "Sava_W_India"  "S_India"       "Ganges_E_Indi" "Chinese_loess" "Japanese"     
[16] "Lower-MiddleY" "South trop ch" "Chinese_loess" "Southwes amaz" "C/S_Andes"    

#subset_order <- c(1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 17, 18)
subset_order <- c(8, 10, 9, 5, 18, 7, 6, 20, 1, 2, 13, 16)
origins_subset <- origins[subset_order,]
origins_subset$CONTINENT

 [1] Mesoamerica   NW_Lowland_SA N_Lowland_SA  Fertile_Cresc Chinese_loess New_Guinea   
 [7] E_North_Ameri C/S_Andes     W_African_Sav Sudanic_Savan Ganges_E_Indi Lower-MiddleY
19 Levels: C/S_Andes Chinese_loess E_North_Ameri Ethipian plat ... West Africa T

d <- readPNG("earth.png")
png(file=paste("40962.png",sep=""),width=2000,height=1000, bg="transparent")
par(mar=c(0,0,0,0))
plot(seq(-180, 180, length.out = 19), seq(-90, 90, length.out = 19), type="n",xlim=c(-180, 180),ylim=c(-90, 90), xaxt="n")

rasterImage(d, -180, -90, 180, 90, interpolate=TRUE, col=d)
polygon(x=c(-180,-180, 180,180), y=c(-90, 90, 90, -90), col=adjustcolor("white", alpha=0.1))
#rasterImage(d, -13.5, -13.5, 375, 375, interpolate=TRUE, col=d)
plot(origins_subset, add=TRUE, col=adjustcolor("white", alpha=.8), xaxt="n", border="white", lwd=4) #still need to reproject!!!
dev.off()

quartz 
     2 

d <- readPNG("40962.png")
dim(d)

[1] 1000 2000    4

par(mar=c(0,0,0,0))

plot(0:360,0:360,type="n",xlim=c(20,360),ylim=c(65,295), yaxt="n", xaxt="n")
rasterImage(d, -28.5, -13.5, 388, 375, interpolate=TRUE, col=d)

axis(2, label=seq(-90, 90, length.out = 19), at=seq(1, 360, length.out = 19), las=1)
mtext("latitude", 2, line=4, at=180)

abline(h=seq(1, 360, length.out = 19), col=adjustcolor("grey10", alpha= 0.4), lwd=1)
abline(h=180, col=adjustcolor("white", alpha= .5), lwd=1)

load('PopD_all_December.rdata')
# Extract the data
prod.origin <- extract(PopD.ALL, origins_subset)

NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files

library(matrixStats)
# Mean and SD per region
means <- lapply(prod.origin, colMeans, na.rm = TRUE)
sds <- lapply(prod.origin, colSds, na.rm = TRUE)
## new values from Bruno's GAM model (produced in script called Population_Trend_per_y.R)
means <- global.means
sds <- global.gams
names(means) <- origins_subset@data$CONTINENT
ymax <- max(unlist(means))
ymin <- min(unlist(means))
time <- 4:21
#plot(origins)
#means[[1]] +
#sds[[1]]
#scale(as.numeric(means[[1]]), center=FALSE)
name_vector <- as.character(origins_subset@data$CONTINENT)
for(i in 1:12){
    
    if(i > 6){frameplot()}else{frameplot_bottom()}
    
        ## customize polygons for each graph
    if(i == 1){ #mesoamerica  #values from Larson
        
            complex_figure(3, i, means, sds)
                
    
        }
    
    
    #########
    if(i == 2 ){ #NW lowlands SA  #values from Larson
        
        complex_figure(6, i, means, sds)
    
        }
        
        #########
    if( i == 3){ #NW lowlands SA  #values from Larson
        
        complex_figure(6, i, means, sds)
        
        }
        #########
    if(i == 4){ #Fertile crescent aka Southwest asia  #values from Larson
        
        
    complex_figure(8, i, means, sds)
                
        }
        
        #########
    if(i == 5){ #loess plateau  #values from Larson
        
        complex_figure(2, i, means, sds)
            
        }
        
        
        #########
    if(i == 6){ #new guinea  #values from Larson
        
        complex_figure(4, i, means, sds)
        
        }
#########
    if(i == 7){ #Eastern N.A.  #values from Larson
        
        complex_figure(5, i, means, sds)
        
            }
        #########
    if(i == 8){ #Andes  #values from Larson
        
        complex_figure(6, i, means, sds)
        
                }
#########
    if(i == 9){ #W. African Sav  #values from Larson
        
        complex_figure(1, i, means, sds)
        
            }
#########
    if(i == 10){ #Sudanic sav  #values from Larson
        
        complex_figure(1, i, means, sds)
        
                }
#########
    if(i == 11){ #Ganges  #values from Larson
        
        
        complex_figure(7, i, means, sds) 
        
        }
#########
    if(i == 12){ #loess  #values from Larson
        
        complex_figure(2, i, means, sds)
         
                }
        
        
        #lines(4:21, means[[i]])
        
        abline(h = 1, col=adjustcolor("forestgreen", alpha=.5), lty=2)
        
    # add axes to some locations
    if(i == 1 | i == 7){axis(2, at=seq(0,2, by=0.25), label=seq(0,2, by=0.25), las=1)}
    if(i == 6 | i == 12){axis(4, at=seq(0,2, by=0.25), label=seq(0,2, by=0.25), las=1)}
    #if(i == 6 | i == 12){axis(4, at=seq(2,3, by=0.25), label=seq(0,1, by=0.25), las=1)
    #   axis(4, at=seq(-1,0, by=0.25), label=rev(seq(0,1, by=0.25)), las=1)
    #   }
    if(i > 6){axis(1)} else{axis(3)}
    
    # add text 
    if(i < 7){polygon(x=c(-30, -30, 30, 30), y=c(-0.1, -0.5, -0.5, -0.1), col="black")
    mtext(name_vector[i], 1, line=-1.2, col="white", cex=0.5)}
    
    if(i > 6){polygon(x=c(-30, -30, 30, 30), y=c(2.1, 2.5, 2.5, 2.1), col="black")
    mtext(name_vector[i], 3, line=-1.2, col="white", cex=0.5)}
    
    # add axis labels
    if(i == 1 | i ==  7){mtext("scaled density potential", 2, line=4, at=1)}
    if(i ==  3){mtext("Thousand years before present", 3, line=3.5, at =5)}
    if(i ==  9){mtext("Thousand years before present", 1, line=3.5, at =5)
        
        }
    
}

Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,      4,      7,      8
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:5] =      0,      1,      4,      7,      8
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,      1,      2,  ...,      6,    6.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,      1,      2,  ...,      6,    6.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:6] =      0,    0.5,      1,  ...,      2,      8
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,    0.5,   0.75,  ...,      5,      7
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,    0.5,      2,  ...,   7.75,    9.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,    0.5,      1,  ...,    4.5,    7.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:1] =      0
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:2] =      0,      4
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:3] =      0,    0.5,   1.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:3] =      0,      1,   1.25
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:8] =      0,      1,      2,  ...,      5,      6
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,      1,      2,  ...,      6,    6.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   0.25,   1.25,  ...,   6.75,   7.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   2.25,   2.75,  ...,   5.75,   6.25
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   0.25,   1.25,  ...,   6.75,   7.75
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,   2.25,   2.75,  ...,   5.75,   6.25
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:7] =      0,    0.5,      1,  ...,      5,    5.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:4] =      0,    3.5,      4,    4.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:11] =      0,    0.5,      2,  ...,   7.75,    9.5
Empirical CDF 
Call: ecdf(maxer - match)
 x[1:9] =      0,    0.5,      1,  ...,    4.5,    7.5

saveToPDF <- function(...) {
    d = dev.copy(pdf,...)
    dev.off(d)
}
saveToPNG <- function(...) {
    d = dev.copy(png,...)
    dev.off(d)
}
## Try them out
saveToPDF("my.pdf", height=8,width=8)

quartz 
     2 

saveToPNG("my.png", height=8, width=8, units="in", res=300)

quartz 
     2 

dev.off()

null device 
          1